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Evolutionary computation to explain deep learning models for time series forecasting

Published: 07 June 2023 Publication History

Abstract

Deep learning has become one of the most useful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, deep learning is known as a black box approach and most experts experience difficulties to explain and interpret deep learning results. In this context, explainable artificial intelligence (XAI) is emerging with the aim of providing black box models with sufficient interpretability so that models can be easily understood by humans. The use of an evolutionary-based association rules extraction algorithm to explain deep learning models for multi-step time series forecasting is addressed in this work. This evolutionary application is proposed to be used with the predictions obtained by long-short term memory (LSTM) deep learning network. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal, showing that almost 98% of the model can be explained.

References

[1]
A. Abanda. 2021. Contributions to Time Series Classification: Meta-Learning and Explainability. Ph.D. Dissertation. University of the Basque Country.
[2]
R. Agrawal, T. Imielinski, and A. Swami. 1993. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data. 207--216.
[3]
A. Anguita-Ruiz, A. Segura-Delgado, R. Alcalá, C. M. Aguilera, and J. Alcalá-Fdez. 2020. explainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research. PLoS Computational Biology 16, 4 (2020), e1007792.
[4]
A. Barredo-Arrieta, N. Díaz-Rodríguez, J. del Ser, et al. 2020. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion 58 (2020), 82--115.
[5]
J. A. Gallardo-Gómez, F. Divina, A. Troncoso, and F. Martínez-Álvarez. 2022. Explainable Artificial Intelligence for the Electric Vehicle Load Demand Forecasting Problem. In Proceedings of the International Conference on Soft Computing Models in Industrial and Environmental Applications. 413--422.
[6]
B. Letham, C. Rudin, T. H. McCormick, and D. Madigan. 2015. Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model. The Annals of Applied Statistics 9, 3 (2015), 1350--1371.
[7]
B. Mahbooba, M. Timilsina, R. Sahal, and M. Serrano. 2021. Explainable artificial intelligence (XAI) to enhance trust management in intrusion detection systems using decision tree model. Complexity 2021 (2021), 6634811.
[8]
M. Martínez-Ballesteros, F. Martínez-Álvarez, A. Troncoso, and J. C. Riquelme. 2011. An evolutionary algorithm to discover quantitative association rules in multidimensional time series. Soft Computing 15, 10 (2011), 2065--2084.
[9]
M. Martínez Ballesteros, A. Troncoso, F. Martínez-Álvarez, and J. C. Riquelme. 2016. Improving a multi-objective evolutionary algorithm to discover quantitative association rules. Knowledge and Information Systems 49 (2016), 481--509.
[10]
G. Pandey, S. Chawla, S. Poon, B. Arunasalam, and J. G. Davis. 2009. Association rules network: Definition and applications. Statistical Analysis and Data Mining 1, 4 (2009), 260--279.
[11]
S. N. Payrovnaziri, Z. Chen, P. Rengifo-Moreno, T. Miller, J. Bian, J. H. Chen, X. Liu, and Z. He. 2020. Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review. Journal of the American Medical Informatics Association 27, 7 (2020), 1173--1185.
[12]
D. Rajapaksha, C. Bergmeir, and W. Buntine. 2020. LoRMIkA: Local rule-based model interpretability with k-optimal associations. Information Sciences 540 (2020), 221--241.
[13]
R. Talavera, R. Pérez-Chacón, M. Martínez-Ballesteros, A. Troncoso, and F. Martínez-Álvarez. 2016. A nearest neighbours-based algorithm for big time series data forecasting. Lecture Notes in Computer Science 5391 (2016), 674--679.
[14]
D. Thi, P. Meysman, and K. Laukens. 2022. MoMAC: Multi-objective optimization to combine multiple association rules into an interpretable classification. Applied Intelligence 52 (2022), 3090--3102.
[15]
J. F. Torres, M. J. Jiménez-Navarro, F. Martínez-Álvarez, and A. Troncoso. 2021. Electricity Consumption Time Series Forecasting Using Temporal Convolutional Networks. In Proceedings of the Conference of the Spanish Association for Artificial Intelligence. 216--225.
[16]
A. R. Troncoso-García, M. Martinez-Ballesteros, F Martínez-Álvarez, and A. Troncoso. 2022. Explainable machine learning for sleep apnea prediction. Procedia Computer Science 207 (2022), 2930--2939.

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      cover image ACM Conferences
      SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
      March 2023
      1932 pages
      ISBN:9781450395175
      DOI:10.1145/3555776
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      Published: 07 June 2023

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      • (2024)Explainable hybrid deep learning and Coronavirus Optimization Algorithm for improving evapotranspiration forecastingComputers and Electronics in Agriculture10.1016/j.compag.2023.108387215:COnline publication date: 27-Feb-2024

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